In [1]:
import pandas as pd
import numpy as np
from Plate_Analysis_BSA import PlateDataset
import matplotlib.pyplot as plt
import re
plate1_t1 = PlateDataset('20191121_BSA_Dopt_plate_1_t1.CSV')
plate2_t1 = PlateDataset('20191121_BSA_Dopt_plate_2_t1.CSV')
plate1_t2 = PlateDataset('20191121_BSA_Dopt_plate_1_t2.CSV')
plate2_t2 = PlateDataset('20191121_BSA_Dopt_plate_2_t2.CSV')
def PlotTracesAndGetMetrics(plate,wrongwayround,i,plan, metrics):
if (i-1)%2==0:
idx = int(i/2)
else:
idx = int((i-1)/2)
row = plan.loc[idx,:]
print(f'{plate.plate_data}\t Trace {i}')
print(f"Protein conc = {round(row['Protein Conc'],2)},\
Vol = {round(row['Protein Vol'],2)}, BSA = {round(row['BSA'],2)} mg/ml")
NormalizedTraces, DifferenceSpec, DiffDiff = plate.AnalysisPipeline_1(i,wrongwayround)
plate.PlotTrace(NormalizedTraces,vol=row['Protein Vol'])
plate.PlotTrace(DifferenceSpec,vol=row['Protein Vol'])
plate.PlotMichaelesMenten(DiffDiff,row['Protein Vol'],'')
concs = plate.CalculateCompoundConcs(4,row['Protein Vol'],5)
km, vmax, loss = plate.FitMichaelisMenten(concs,DiffDiff)
Noise = DifferenceSpec.loc[:,405].std()
temp = pd.Series([km.item(), vmax.item(), loss.item(),Noise],index = ['Km','Vmax','Loss','Noise'])
temp = temp.append(row)
t = plate.metadata.iloc[0,2]
t = re.search(r'\d\d:\d\d:\d\d',t)[0]
temp['Time'] = t
temp = pd.DataFrame(temp).T
return temp
# Keeping track of which ones I messed up
WrongWayRound_P1 = {1:False,2:False,3:False,4:False,
5:False,6:False,7:False,8:False,
9:False,10:False,11:False,12:False,
13:False, 14:False, 15:False, 16:False,
17:False,18:False, 19:False,20:False}
WrongWayRound_P2 = {1:True,2:True,3:False,4:False,
5:False,6:False,7:False,8:False,
9:False,10:False,11:False,12:False,
13:False, 14:False, 15:False, 16:False,
17:False,18:False, 19:False,20:False}
plan = pd.read_csv('BSA_Dopt.csv',index_col = 0)
metrics = pd.DataFrame([],columns=['Km', 'Vmax', 'Loss', 'Noise','Protein Conc', 'Protein Vol', 'BSA'])
for i in range(1,21):
metrics = metrics.append(PlotTracesAndGetMetrics(plate1_t1,WrongWayRound_P1[i],i,plan,metrics),sort=True)
metrics = metrics.append(PlotTracesAndGetMetrics(plate2_t1,WrongWayRound_P2[i],i,plan,metrics),sort=True)
metrics = metrics.append(PlotTracesAndGetMetrics(plate1_t2,WrongWayRound_P1[i],i,plan,metrics),sort=True)
metrics = metrics.append(PlotTracesAndGetMetrics(plate2_t2,WrongWayRound_P2[i],i,plan,metrics),sort=True)
20191121_BSA_Dopt_plate_1_t1.CSV Trace 1 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 1 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 1 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 1 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 2 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 2 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 2 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 2 Protein conc = 10.0, Vol = 23.64, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 3 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 3 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 3 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 3 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 4 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 4 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 4 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 4 Protein conc = 16.36, Vol = 30.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 5 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 5 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 5 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 5 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 6 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 6 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 6 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 6 Protein conc = 10.0, Vol = 30.0, BSA = 0.39 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 7 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 7 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 7 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 7 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 8 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 8 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 8 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 8 Protein conc = 10.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 9 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 9 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 9 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 9 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 10 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 10 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 10 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 10 Protein conc = 20.0, Vol = 20.0, BSA = 0.1 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 11 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 11 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 11 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 11 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 12 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 12 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 12 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 12 Protein conc = 20.0, Vol = 30.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 13 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 13 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 13 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 13 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 14 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 14 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 14 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 14 Protein conc = 14.55, Vol = 25.45, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 15 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 15 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 15 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 15 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 16 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 16 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 16 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 16 Protein conc = 20.0, Vol = 20.0, BSA = 0.5 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 17 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 17 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 17 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 17 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 18 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 18 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 18 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 18 Protein conc = 13.64, Vol = 20.0, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 19 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 19 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 19 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 19 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t1.CSV Trace 20 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t1.CSV Trace 20 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_1_t2.CSV Trace 20 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
20191121_BSA_Dopt_plate_2_t2.CSV Trace 20 Protein conc = 20.0, Vol = 26.36, BSA = 0.25 mg/ml
In [9]:
metrics
Out[9]:
| BSA | Km | Loss | Noise | Protein Conc | Protein Vol | Time | Vmax | R^2 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.1 | 30.8745 | 0.00081259 | 0.0343249 | 10 | 23.6364 | 18:56:54 | 0.139563 | 0.999187 |
| 1 | 0.1 | 232.06 | 0.011795 | 0.154616 | 10 | 23.6364 | 19:03:19 | 0.0110878 | 0.988205 |
| 2 | 0.1 | 29.2995 | 0.000717402 | 0.0239305 | 10 | 23.6364 | 20:05:07 | 0.122247 | 0.999283 |
| 3 | 0.1 | 150.106 | 0.0131484 | 0.110479 | 10 | 23.6364 | 20:22:52 | 0.0119218 | 0.986852 |
| 4 | 0.1 | 41.8421 | 0.000578105 | 0.00999615 | 10 | 23.6364 | 18:56:54 | 0.127217 | 0.999422 |
| 5 | 0.1 | 37.3214 | 0.000648081 | 0.00777736 | 10 | 23.6364 | 19:03:19 | 0.115208 | 0.999352 |
| 6 | 0.1 | 39.2692 | 0.000557899 | 0.00856901 | 10 | 23.6364 | 20:05:07 | 0.118726 | 0.999442 |
| 7 | 0.1 | 35.9658 | 0.000642657 | 0.00763128 | 10 | 23.6364 | 20:22:52 | 0.110195 | 0.999357 |
| 8 | 0.1 | 19.1683 | 0.000954688 | 0.00798016 | 16.3636 | 30 | 18:56:54 | 0.163422 | 0.999045 |
| 9 | 0.1 | 4.96988 | 0.000783622 | 0.049939 | 16.3636 | 30 | 19:03:19 | 0.154561 | 0.999216 |
| 10 | 0.1 | 19.2278 | 0.0009166 | 0.00912132 | 16.3636 | 30 | 20:05:07 | 0.153821 | 0.999083 |
| 11 | 0.1 | 4.74124 | 0.000809669 | 0.0819529 | 16.3636 | 30 | 20:22:52 | 0.156947 | 0.99919 |
| 12 | 0.1 | 24.2011 | 0.00110906 | 0.00971032 | 16.3636 | 30 | 18:56:54 | 0.141128 | 0.998891 |
| 13 | 0.1 | 2.85163 | 0.00141889 | 0.0345163 | 16.3636 | 30 | 19:03:19 | 0.188609 | 0.998581 |
| 14 | 0.1 | 26.6453 | 0.00109023 | 0.00582761 | 16.3636 | 30 | 20:05:07 | 0.143447 | 0.99891 |
| 15 | 0.1 | 2.28127 | 0.00141668 | 0.0408773 | 16.3636 | 30 | 20:22:52 | 0.168349 | 0.998583 |
| 16 | 0.390909 | 18.9843 | 0.00065136 | 0.00986076 | 10 | 30 | 18:56:54 | 0.114458 | 0.999349 |
| 17 | 0.390909 | 1.55374 | 0.000447094 | 0.0934706 | 10 | 30 | 19:03:19 | 0.0872795 | 0.999553 |
| 18 | 0.390909 | 20.8108 | 0.000641704 | 0.00921142 | 10 | 30 | 20:05:07 | 0.106763 | 0.999358 |
| 19 | 0.390909 | 1.74219 | 0.00045377 | 0.0881186 | 10 | 30 | 20:22:52 | 0.0891327 | 0.999546 |
| 20 | 0.390909 | 38.5289 | 0.0007146 | 0.00549278 | 10 | 30 | 18:56:54 | 0.134285 | 0.999285 |
| 21 | 0.390909 | 43.2395 | 0.000612974 | 0.00240404 | 10 | 30 | 19:03:19 | 0.123123 | 0.999387 |
| 22 | 0.390909 | 38.6205 | 0.000721574 | 0.00563591 | 10 | 30 | 20:05:07 | 0.130129 | 0.999278 |
| 23 | 0.390909 | 43.2428 | 0.000612676 | 0.00231798 | 10 | 30 | 20:22:52 | 0.121092 | 0.999387 |
| 24 | 0.5 | 72.7236 | 0.000362873 | 0.0057914 | 10 | 20 | 18:56:54 | 0.0749823 | 0.999637 |
| 25 | 0.5 | 111.07 | 0.0168197 | 0.0362681 | 10 | 20 | 19:03:19 | 0.0600884 | 0.98318 |
| 26 | 0.5 | 68.7213 | 0.000353813 | 0.00630106 | 10 | 20 | 20:05:07 | 0.0711239 | 0.999646 |
| 27 | 0.5 | -9.56217 | 0.0124229 | 0.0343541 | 10 | 20 | 20:22:52 | 0.0240258 | 0.987577 |
| 28 | 0.5 | 85.1302 | 0.000244677 | 0.00438908 | 10 | 20 | 18:56:54 | 0.098922 | 0.999755 |
| 29 | 0.5 | 85.7171 | 0.000217676 | 0.00223993 | 10 | 20 | 19:03:19 | 0.0952402 | 0.999782 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 50 | 0.5 | 24.3299 | 0.000576377 | 0.00857154 | 14.5455 | 25.4545 | 20:05:07 | 0.111085 | 0.999424 |
| 51 | 0.5 | -111.355 | 0.0585446 | 0.0668969 | 14.5455 | 25.4545 | 20:22:52 | 0.0135055 | 0.941455 |
| 52 | 0.5 | 121.338 | 0.000166357 | 0.0053329 | 14.5455 | 25.4545 | 18:56:54 | 0.135136 | 0.999834 |
| 53 | 0.5 | 75.1172 | 0.000520825 | 0.0015573 | 14.5455 | 25.4545 | 19:03:19 | 0.136139 | 0.999479 |
| 54 | 0.5 | 121.33 | 0.000157952 | 0.00565094 | 14.5455 | 25.4545 | 20:05:07 | 0.12946 | 0.999842 |
| 55 | 0.5 | 75.8192 | 0.000503302 | 0.00354868 | 14.5455 | 25.4545 | 20:22:52 | 0.131302 | 0.999497 |
| 56 | 0.5 | 15.475 | 0.00041455 | 0.00760371 | 20 | 20 | 18:56:54 | 0.0882054 | 0.999585 |
| 57 | 0.5 | 17.3093 | 0.000207663 | 0.0497924 | 20 | 20 | 19:03:19 | 0.0668582 | 0.999792 |
| 58 | 0.5 | 15.5041 | 0.000383317 | 0.00910187 | 20 | 20 | 20:05:07 | 0.0823346 | 0.999617 |
| 59 | 0.5 | 15.0128 | 0.000215769 | 0.0512561 | 20 | 20 | 20:22:52 | 0.0616886 | 0.999784 |
| 60 | 0.5 | 118.714 | 0.000138223 | 0.00244178 | 20 | 20 | 18:56:54 | 0.102316 | 0.999862 |
| 61 | 0.5 | 95.9729 | 0.000224233 | 0.00109385 | 20 | 20 | 19:03:19 | 0.0945815 | 0.999776 |
| 62 | 0.5 | 115.417 | 0.000147581 | 0.0026702 | 20 | 20 | 20:05:07 | 0.0974681 | 0.999852 |
| 63 | 0.5 | 91.7699 | 0.000229537 | 0.000702484 | 20 | 20 | 20:22:52 | 0.089001 | 0.99977 |
| 64 | 0.245455 | 0.404614 | 0.00139666 | 0.0406971 | 13.6364 | 20 | 18:56:54 | 0.0377183 | 0.998603 |
| 65 | 0.245455 | 14.9002 | 0.000180602 | 0.0586098 | 13.6364 | 20 | 19:03:19 | 0.0511664 | 0.999819 |
| 66 | 0.245455 | 100.165 | 0.0145702 | 0.0389312 | 13.6364 | 20 | 20:05:07 | 0.0550925 | 0.98543 |
| 67 | 0.245455 | 16.0129 | 0.000181854 | 0.0549738 | 13.6364 | 20 | 20:22:52 | 0.0504616 | 0.999818 |
| 68 | 0.245455 | 54.1648 | 0.000413418 | 0.00216164 | 13.6364 | 20 | 18:56:54 | 0.110694 | 0.999587 |
| 69 | 0.245455 | 63.2541 | 0.000316024 | 0.00517409 | 13.6364 | 20 | 19:03:19 | 0.10053 | 0.999684 |
| 70 | 0.245455 | 52.7028 | 0.000406325 | 0.0015967 | 13.6364 | 20 | 20:05:07 | 0.103118 | 0.999594 |
| 71 | 0.245455 | 63.3378 | 0.000296175 | 0.00447964 | 13.6364 | 20 | 20:22:52 | 0.0955798 | 0.999704 |
| 72 | 0.245455 | 0.686156 | 0.00121146 | 0.074163 | 20 | 26.3636 | 18:56:54 | 0.08109 | 0.998789 |
| 73 | 0.245455 | 18.6757 | 0.000474989 | 0.0959057 | 20 | 26.3636 | 19:03:19 | 0.122646 | 0.999525 |
| 74 | 0.245455 | -109.508 | 0.059561 | 0.0720769 | 20 | 26.3636 | 20:05:07 | 0.0145383 | 0.940439 |
| 75 | 0.245455 | 18.3991 | 0.000478029 | 0.0895894 | 20 | 26.3636 | 20:22:52 | 0.118888 | 0.999522 |
| 76 | 0.245455 | 54.661 | 0.000593364 | 0.00567433 | 20 | 26.3636 | 18:56:54 | 0.15806 | 0.999407 |
| 77 | 0.245455 | 51.8335 | 0.000670075 | 0.00418459 | 20 | 26.3636 | 19:03:19 | 0.152946 | 0.99933 |
| 78 | 0.245455 | 55.5967 | 0.00056386 | 0.00432127 | 20 | 26.3636 | 20:05:07 | 0.151863 | 0.999436 |
| 79 | 0.245455 | 51.5148 | 0.000657558 | 0.00282488 | 20 | 26.3636 | 20:22:52 | 0.148743 | 0.999342 |
80 rows × 9 columns
In [10]:
metrics.reset_index(inplace=True,drop=True)
metrics['R^2'] = 1-metrics['Loss']
metrics.to_csv('BSA_Dopt_metrics.csv')
In [8]:
def PlotHist(df, colname):
plt.figure(figsize = (5,4))
plt.hist(df[colname],bins=20,alpha=0.8)
plt.xlabel(colname)
plt.ylabel('Frequency')
plt.show()
for i in ['Km','Vmax','R^2']:
PlotHist(metrics,i)
In [ ]: